The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
The technology disclosed improves existing streaming processing systems by providing the ability to both scale up and scale down resources within an infrastructure of a stream processing system. In particular, the technology disclosed relates to a dispatch process for a stream processing system that adapts its behavior according to a computational capacity of the system based on a run-time evaluation. The technical solution includes, during run-time execution of a pipeline, comparing a count of available physical threads against a set number of logically parallel threads; and when a count of available physical threads equals or exceeds the number of logically parallel threads, concurrently processing the batches at the physical threads. Further, when there are fewer available physical threads than the number of logically parallel threads, the solution includes multiplexing the batches sequentially over the available physical threads.
In today's world, we are dealing with huge data volumes, popularly referred to as “Big Data”. Web applications that serve and manage millions of Internet users, such as Facebook™, Instagram™, Twitter™, banking websites, or even online retail shops, such as Amazon.com™ or eBay™ are faced with the challenge of ingesting high volumes of data as fast as possible so that the end users can be provided with a real-time experience.
Another major contributor to Big Data is a concept and paradigm called “Internet of Things” (IoT). IoT is about a pervasive presence in the environment of a variety of things/objects that through wireless and wired connections are able to interact with each other and cooperate with other things/objects to create new applications/services. These applications/services are in areas likes smart cities (regions), smart car and mobility, smart home and assisted living, smart industries, public safety, energy and environmental protection, agriculture and tourism.
Stream processing is quickly becoming a crucial component of Big Data processing solutions for enterprises, with many popular open-source stream processing systems available today, including Apache Storm™, Apache Spark™, Apache Samza™ Apache Flink™, and others. Many of these stream processing solutions offer default schedulers that evenly distribute processing tasks between the available computation resources using a round-robin strategy. However, such a strategy is not cost effective because substantial computation time and resources are lost during assignment and re-assignment of tasks to the correct sequence of computation resources in the stream processing system, thereby introducing significant latency in the system.
Therefore, an opportunity arises to provide systems and methods that use a combination of concurrent and multiplexed processing schemes to adapt to the varying computational requirements and availability in a stream processing system with little performance loss or added complexity. Increased revenue, higher user retention, improved user engagement and experience may result.